Researchers have developed tools to improve fMRI data analysis that may pave the way to improving schizophrenia treatment.
A method of image analysis developed by researchers at the University of Maryland, Baltimore County (UMBC) in the United States is called independent vector analysis (IVA) for joint subspace extraction (CS).
Through this method, they were able to classify subsets of functional MRI data based on brain activity only, thus proving that there is a link between brain activity and some mental illnesses, according to the study published in the journal NeuroImage.
Schizophrenia patients use functional MRI data
In particular, they were able to identify subgroups of schizophrenic patients using the functional MRI data they analyzed. Previously, there was no clear way to collect schizophrenia in patients based on brain imaging alone, but methods developed by UMBC researchers showed that there was a significant correlation between patient brain activity and diagnoses.
“The most exciting part is that we have discovered that specific subgroups have clinical significance by looking at their diagnostic symptoms,” explains Qunfang Long, Ph.D. UMBC candidate.
“This result encouraged us to do more to study the subtypes of patients with schizophrenia using neuroimaging data.”
Their work can help diagnose and treat patients with mental illness that are difficult to identify. It may also be shown to medical practitioners whether current treatments work or not based on image groups.
“Now that data-driven methods have gained popularity, the biggest challenge has been to capture variance for each subject while simultaneously analyzing fMRI data sets from a large number of subjects,” said UdayBC professor Tolay Adali.
“We can now conduct this analysis effectively, and we can define targeted groups of topics,” Adali said.